Model Optimization, Debugging, and Performance Tuning Questions

Techniques for optimizing machine learning models in production, including hyperparameter tuning, architecture optimization (e.g., pruning, quantization, distillation), and hardware acceleration. Covers profiling and optimizing inference latency, throughput, memory usage, and energy consumption; debugging training instabilities and inference issues; diagnosing data-related problems; ensuring reproducibility and reliability in ML pipelines; and implementing serving optimizations (batching, caching, parallelization) within ML platforms and MLOps workflows.